
import unittest

from transformers import PegasusTokenizer, PegasusTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


snake_case__					=				get_tests_dir("""fixtures/test_sentencepiece_no_bos.model""")







@require_sentencepiece
@require_tokenizers
class 				UpperCAmelCase						( _lowerCAmelCase    ,			unittest.TestCase       ):
					a__:			Dict      	=					PegasusTokenizer
					a__:			Optional[Any]      	=					PegasusTokenizerFast
					a__:			Optional[int]      	=					True
					a__:			Optional[Any]      	=					True

					def 							_lowerCAmelCase						(      self   :       Union[str, Any]       ):
											super().setUp()

											# We have a SentencePiece fixture for testing
											lowercase							:     Optional[Any]									=	PegasusTokenizer(SCREAMING_SNAKE_CASE_       )
											tokenizer.save_pretrained(self.tmpdirname       )

					@cached_property
					def 							_lowerCAmelCase						(      self   :       Tuple       ):
											return PegasusTokenizer.from_pretrained('''google/pegasus-large'''       )

					def 							_lowerCAmelCase						(      self   :       Tuple			,   **lowerCAmelCase   :       Any       ):
											return PegasusTokenizer.from_pretrained(self.tmpdirname			,   **SCREAMING_SNAKE_CASE_       )

					def 							_lowerCAmelCase						(      self   :       Tuple			,   lowerCAmelCase   :       Any       ):
											return ("This is a test", "This is a test")

					def 							_lowerCAmelCase						(      self   :       str       ):
											lowercase							:     List[str]									=	"""</s>"""
											lowercase							:     str									=	1

											self.assertEqual(self.get_tokenizer()._convert_token_to_id(SCREAMING_SNAKE_CASE_       )			,   SCREAMING_SNAKE_CASE_       )
											self.assertEqual(self.get_tokenizer()._convert_id_to_token(SCREAMING_SNAKE_CASE_       )			,   SCREAMING_SNAKE_CASE_       )

					def 							_lowerCAmelCase						(      self   :       Optional[int]       ):
											lowercase							:     Tuple									=	list(self.get_tokenizer().get_vocab().keys()       )

											self.assertEqual(vocab_keys[0]			,   '''<pad>'''       )
											self.assertEqual(vocab_keys[1]			,   '''</s>'''       )
											self.assertEqual(vocab_keys[-1]			,   '''v'''       )
											self.assertEqual(len(SCREAMING_SNAKE_CASE_       )			,   1103       )

					def 							_lowerCAmelCase						(      self   :       List[Any]       ):
											self.assertEqual(self.get_tokenizer().vocab_size			,   1103       )

					def 							_lowerCAmelCase						(      self   :       List[Any]       ):
											lowercase							:     Tuple									=	self.rust_tokenizer_class.from_pretrained(self.tmpdirname       )
											lowercase							:     Union[str, Any]									=	self.tokenizer_class.from_pretrained(self.tmpdirname       )
											lowercase							:     Optional[int]									=	(
											    """Let's see which <unk> is the better <unk_token_11> one <mask_1> It seems like this <mask_2> was important"""
											    """ </s> <pad> <pad> <pad>"""
											)
											lowercase							:     Any									=	rust_tokenizer([raw_input_str]			,   return_tensors=SCREAMING_SNAKE_CASE_			,   add_special_tokens=SCREAMING_SNAKE_CASE_       ).input_ids[0]
											lowercase							:     List[str]									=	py_tokenizer([raw_input_str]			,   return_tensors=SCREAMING_SNAKE_CASE_			,   add_special_tokens=SCREAMING_SNAKE_CASE_       ).input_ids[0]
											self.assertListEqual(SCREAMING_SNAKE_CASE_			,   SCREAMING_SNAKE_CASE_       )

					def 							_lowerCAmelCase						(      self   :       Union[str, Any]       ):
											lowercase							:     List[Any]									=	self._large_tokenizer
											# <mask_1> masks whole sentence while <mask_2> masks single word
											lowercase							:     Any									=	"""<mask_1> To ensure a <mask_2> flow of bank resolutions."""
											lowercase							:     Union[str, Any]									=	[2, 413, 615, 114, 3, 1971, 113, 1679, 1_0710, 107, 1]
											lowercase							:     List[Any]									=	tokenizer([raw_input_str]			,   return_tensors=SCREAMING_SNAKE_CASE_       ).input_ids[0]
											self.assertListEqual(SCREAMING_SNAKE_CASE_			,   SCREAMING_SNAKE_CASE_       )

					def 							_lowerCAmelCase						(      self   :       Optional[int]       ):
											lowercase							:     Any									=	self._large_tokenizer
											# The tracebacks for the following asserts are **better** without messages or self.assertEqual
											assert tokenizer.vocab_size == 9_6103
											assert tokenizer.pad_token_id == 0
											assert tokenizer.eos_token_id == 1
											assert tokenizer.offset == 103
											assert tokenizer.unk_token_id == tokenizer.offset + 2 == 105
											assert tokenizer.unk_token == "<unk>"
											assert tokenizer.model_max_length == 1024
											lowercase							:     List[str]									=	"""To ensure a smooth flow of bank resolutions."""
											lowercase							:     List[str]									=	[413, 615, 114, 2291, 1971, 113, 1679, 1_0710, 107, 1]
											lowercase							:     Optional[int]									=	tokenizer([raw_input_str]			,   return_tensors=SCREAMING_SNAKE_CASE_       ).input_ids[0]
											self.assertListEqual(SCREAMING_SNAKE_CASE_			,   SCREAMING_SNAKE_CASE_       )
											assert tokenizer.convert_ids_to_tokens([0, 1, 2, 3]       ) == ["<pad>", "</s>", "<mask_1>", "<mask_2>"]

					@require_torch
					def 							_lowerCAmelCase						(      self   :       Union[str, Any]       ):
											lowercase							:     Union[str, Any]									=	["""This is going to be way too long.""" * 150, """short example"""]
											lowercase							:     List[Any]									=	["""not super long but more than 5 tokens""", """tiny"""]
											lowercase							:     str									=	self._large_tokenizer(SCREAMING_SNAKE_CASE_			,   padding=SCREAMING_SNAKE_CASE_			,   truncation=SCREAMING_SNAKE_CASE_			,   return_tensors='''pt'''       )
											lowercase							:     Dict									=	self._large_tokenizer(
											    text_target=SCREAMING_SNAKE_CASE_			,   max_length=5			,   padding=SCREAMING_SNAKE_CASE_			,   truncation=SCREAMING_SNAKE_CASE_			,   return_tensors='''pt'''       )

											assert batch.input_ids.shape == (2, 1024)
											assert batch.attention_mask.shape == (2, 1024)
											assert targets["input_ids"].shape == (2, 5)
											assert len(SCREAMING_SNAKE_CASE_       ) == 2  # input_ids, attention_mask.




					@slow
					def 							_lowerCAmelCase						(      self   :       str       ):
											# fmt: off
											lowercase							:     int									=	{"""input_ids""": [[3_8979, 143, 1_8485, 606, 130, 2_6669, 8_7686, 121, 5_4189, 1129, 111, 2_6669, 8_7686, 121, 9114, 1_4787, 121, 1_3249, 158, 592, 956, 121, 1_4621, 3_1576, 143, 6_2613, 108, 9688, 930, 4_3430, 1_1562, 6_2613, 304, 108, 1_1443, 897, 108, 9314, 1_7415, 6_3399, 108, 1_1443, 7614, 1_8316, 118, 4284, 7148, 1_2430, 143, 1400, 2_5703, 158, 111, 4284, 7148, 1_1772, 143, 2_1297, 1064, 158, 122, 204, 3506, 1754, 1133, 1_4787, 1581, 115, 3_3224, 4482, 111, 1355, 110, 2_9173, 317, 5_0833, 108, 2_0147, 9_4665, 111, 7_7198, 107, 1], [110, 6_2613, 117, 638, 112, 1133, 121, 2_0098, 1355, 7_9050, 1_3872, 135, 1596, 5_3541, 1352, 141, 1_3039, 5542, 124, 302, 518, 111, 268, 2956, 115, 149, 4427, 107, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [139, 1235, 2799, 1_8289, 1_7780, 204, 109, 9474, 1296, 107, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], """attention_mask""": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}  # noqa: E501
											# fmt: on

											self.tokenizer_integration_test_util(
											    expected_encoding=SCREAMING_SNAKE_CASE_			,   model_name='''google/bigbird-pegasus-large-arxiv'''			,   revision='''ba85d0851d708441f91440d509690f1ab6353415'''			,   )







@require_sentencepiece
@require_tokenizers
class 				UpperCAmelCase						( _lowerCAmelCase    ,			unittest.TestCase       ):
					a__:			str      	=					PegasusTokenizer
					a__:			Optional[Any]      	=					PegasusTokenizerFast
					a__:			Optional[Any]      	=					True
					a__:			List[str]      	=					True

					def 							_lowerCAmelCase						(      self   :       Optional[Any]       ):
											super().setUp()

											# We have a SentencePiece fixture for testing
											lowercase							:     Tuple									=	PegasusTokenizer(SCREAMING_SNAKE_CASE_			,   offset=0			,   mask_token_sent=SCREAMING_SNAKE_CASE_			,   mask_token='''[MASK]'''       )
											tokenizer.save_pretrained(self.tmpdirname       )

					@cached_property
					def 							_lowerCAmelCase						(      self   :       Union[str, Any]       ):
											return PegasusTokenizer.from_pretrained('''google/bigbird-pegasus-large-arxiv'''       )

					def 							_lowerCAmelCase						(      self   :       Any			,   **lowerCAmelCase   :       Optional[int]       ):
											return PegasusTokenizer.from_pretrained(self.tmpdirname			,   **SCREAMING_SNAKE_CASE_       )

					def 							_lowerCAmelCase						(      self   :       Optional[int]			,   lowerCAmelCase   :       Tuple       ):
											return ("This is a test", "This is a test")

					def 							_lowerCAmelCase						(      self   :       Optional[Any]       ):
											lowercase							:     Any									=	self.rust_tokenizer_class.from_pretrained(self.tmpdirname       )
											lowercase							:     str									=	self.tokenizer_class.from_pretrained(self.tmpdirname       )
											lowercase							:     Union[str, Any]									=	(
											    """Let's see which <unk> is the better <unk_token> one [MASK] It seems like this [MASK] was important </s>"""
											    """ <pad> <pad> <pad>"""
											)
											lowercase							:     Optional[int]									=	rust_tokenizer([raw_input_str]			,   return_tensors=SCREAMING_SNAKE_CASE_			,   add_special_tokens=SCREAMING_SNAKE_CASE_       ).input_ids[0]
											lowercase							:     Union[str, Any]									=	py_tokenizer([raw_input_str]			,   return_tensors=SCREAMING_SNAKE_CASE_			,   add_special_tokens=SCREAMING_SNAKE_CASE_       ).input_ids[0]
											self.assertListEqual(SCREAMING_SNAKE_CASE_			,   SCREAMING_SNAKE_CASE_       )

					@require_torch
					def 							_lowerCAmelCase						(      self   :       List[Any]       ):
											lowercase							:     int									=	["""This is going to be way too long.""" * 1000, """short example"""]
											lowercase							:     str									=	["""not super long but more than 5 tokens""", """tiny"""]
											lowercase							:     str									=	self._large_tokenizer(SCREAMING_SNAKE_CASE_			,   padding=SCREAMING_SNAKE_CASE_			,   truncation=SCREAMING_SNAKE_CASE_			,   return_tensors='''pt'''       )
											lowercase							:     Union[str, Any]									=	self._large_tokenizer(
											    text_target=SCREAMING_SNAKE_CASE_			,   max_length=5			,   padding=SCREAMING_SNAKE_CASE_			,   truncation=SCREAMING_SNAKE_CASE_			,   return_tensors='''pt'''       )

											assert batch.input_ids.shape == (2, 4096)
											assert batch.attention_mask.shape == (2, 4096)
											assert targets["input_ids"].shape == (2, 5)
											assert len(SCREAMING_SNAKE_CASE_       ) == 2  # input_ids, attention_mask.




					def 							_lowerCAmelCase						(      self   :       Optional[int]       ):
											lowercase							:     Union[str, Any]									=	(
											    """This is an example string that is used to test the original TF implementation against the HF"""
											    """ implementation"""
											)

											lowercase							:     Union[str, Any]									=	self._large_tokenizer(SCREAMING_SNAKE_CASE_       ).input_ids

											self.assertListEqual(
											    SCREAMING_SNAKE_CASE_			,   [182, 117, 142, 587, 4211, 120, 117, 263, 112, 804, 109, 856, 2_5016, 3137, 464, 109, 2_6955, 3137, 1]			,   )







import logging
from dataclasses import dataclass, field
from typing import Optional

from seqaseq_trainer import arg_to_scheduler

from transformers import TrainingArguments


a      :  Optional[int]			     =			logging.getLogger(__name__)







@dataclass
class 				_a				(   _lowerCAmelCase					):
			A      		=	field(
			    default=0.0 ,		metadata={'''help''': '''The label smoothing epsilon to apply (if not zero).'''}					)
			A      		=	field(default=_lowerCAmelCase ,		metadata={'''help''': '''Whether to SortishSamler or not.'''}					)
			A      		=	field(
			    default=_lowerCAmelCase ,		metadata={'''help''': '''Whether to use generate to calculate generative metrics (ROUGE, BLEU).'''}					)
			A      		=	field(default=_lowerCAmelCase ,		metadata={'''help''': '''whether to use adafactor'''}					)
			A      		=	field(
			    default=_lowerCAmelCase ,		metadata={'''help''': '''Encoder layer dropout probability. Goes into model.config.'''}					)
			A      		=	field(
			    default=_lowerCAmelCase ,		metadata={'''help''': '''Decoder layer dropout probability. Goes into model.config.'''}					)
			A      		=	field(default=_lowerCAmelCase ,		metadata={'''help''': '''Dropout probability. Goes into model.config.'''}					)
			A      		=	field(
			    default=_lowerCAmelCase ,		metadata={'''help''': '''Attention dropout probability. Goes into model.config.'''}					)
			A      		=	field(
			    default='''linear''' ,		metadata={'''help''': F'''Which lr scheduler to use. Selected in {sorted(arg_to_scheduler.keys()					)}'''} ,		)






